This paper aims to present an intelligent system for tracking moving objects (such as vehicles, persons etc) based on a network of autonomous tracking units that capture and process images from one or more pre-calibrated visual sensors. The proposed system, which has been developed within the framework of TRAVIS (TRAffic VISual monitoring) project, is flexible, scalable and can be applied in a broad field of applications in the future. By the end of the project, two prototypes will be developed, each focusing on different traffic monitoring applications, such as the traffic control of aircraft parking areas at airports and tunnels at highways. First experimental results of the proposed system using two different data fusion techniques are also presented in the paper.
We propose a novel video sensor for real-time motion detection at specific user-defined regions of interest, designed primarily for traffic monitoring, surveillance and tracking applications. Specifically, the new sensor a) supports virtual detectors with a generalized (polygonal) shape, thus providing additional flexibility in the design of detector configurations, b) is based on fast implementations of recent state-of-the art background extraction and update techniques and c) constitutes a generic, inexpensive software solution, which can be used with any video camera. First experimental results confirm that the new video sensor meets the expectations in terms of real-time performance and demonstrates the additional functionalities, according to which it was designed. The final goal is to use this new sensor as an alternative, improved version of embedded motion detection video sensors (like Autoscope®).
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